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Optimization Algorithms: AI techniques for design, planning, and control problems
by Alaa KhamisSolve design, planning, and control problems using modern AI techniques.Optimization problems are everywhere in daily life. What&’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: • The core concepts of search and optimization • Deterministic and stochastic optimization techniques • Graph search algorithms • Trajectory-based optimization algorithms • Evolutionary computing algorithms • Swarm intelligence algorithms • Machine learning methods for search and optimization problems • Efficient trade-offs between search space exploration and exploitation • State-of-the-art Python libraries for search and optimization Inside this comprehensive guide, you&’ll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic algorithms and machine learning methods. Don&’t worry—there&’s no complex mathematical notation. You&’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm. About the technology Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how. About the book Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges. What's inside • Graph search algorithms • Metaheuristic algorithms • Machine learning methods • State-of-the-art Python libraries for optimization • Efficient trade-offs between search space exploration and exploitation About the reader Requires intermediate Python and machine learning skills. About the author Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto. The technical editor on this book was Frances Buontempo. Table of Contents PART 1 1 Introduction to search and optimization 2 A deeper look at search and optimization 3 Blind search algorithms 4 Informed search algorithms PART 2 5 Simulated annealing 6 Tabu search PART 3 7 Genetic algorithms 8 Genetic algorithm variants PART 4 9 Particle swarm optimization 10 Other swarm intelligence algorithms to explore PART 5 11 Supervised and unsupervised learning 12 Reinforcement learning Appendix A Appendix B Appendix C
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
by Gauri JoshiThis book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Optimization and Applications: 9th International Conference, OPTIMA 2018, Petrovac, Montenegro, October 1–5, 2018, Revised Selected Papers (Communications in Computer and Information Science #974)
by Yury Evtushenko Milojica Jaćimović Michael Khachay Yury Kochetov Vlasta Malkova Mikhail PosypkinThis book constitutes the refereed proceedings of the 9th International Conference on Optimization and Applications, OPTIMA 2018, held in Petrovac, Montenegro, in October 2018.The 35 revised full papers and the one short paper presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on mathematical programming; combinatorial and discrete optimization; optimal control; optimization in economy, finance and social sciences; applications.
Optimization and Applications: 10th International Conference, OPTIMA 2019, Petrovac, Montenegro, September 30 – October 4, 2019, Revised Selected Papers (Communications in Computer and Information Science #1145)
by Michael Khachay Milojica Jaćimović Vlasta Malkova Mikhail PosypkinThis book constitutes the refereed proceedings of the 10th International Conference on Optimization and Applications, OPTIMA 2019, held in Petrovac, Montenegro, in September-October 2019.The 35 revised full papers presented were carefully reviewed and selected from 117 submissions. The papers cover such topics as optimization, operations research, optimal control, game theory, and their numerous applications in practical problems of operations research, data analysis, and software development.
Optimization and Applications: 14th International Conference, OPTIMA 2023, Petrovac, Montenegro, September 18–22, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14395)
by Nicholas Olenev Yuri Evtushenko Milojica Jaćimović Michael Khachay Vlasta MalkovaThis book constitutes the refereed proceedings of the 14th International Conference on Optimization and Applications, OPTIMA 2023, held in Petrovac, Montenegro, during September 18–22, 2023.The 27 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: mathematical programming; global optimization; discrete and combinatorial optimization; game theory and mathematical economics; optimization in economics and finance; and applications.
Optimization and Applications: 15th International Conference, OPTIMA 2024, Petrovac, Montenegro, September 16–20, 2024, Revised Selected Papers (Lecture Notes in Computer Science #15218)
by Nicholas Olenev Yuri Evtushenko Milojica Jaćimović Michael Khachay Vlasta MalkovaThis book constitutes the refereed proceedings of the 15th International Conference on Optimization and Applications, OPTIMA 2024, held in Petrovac, Montenegro, during September 16–20, 2024. The 24 full papers presented in this volume were carefully reviewed and selected from 60 submissions. They are grouped into the following topics: Mathematical Programming; Global Optimization; Optimal Control; Game Theory and Mathematical Economics; Optimization in Economics and Finance; and Applications.
Optimization and Applications: 11th International Conference, OPTIMA 2020, Moscow, Russia, September 28 – October 2, 2020, Proceedings (Lecture Notes in Computer Science #12422)
by Nicholas Olenev Yuri Evtushenko Michael Khachay Vlasta MalkovaThis book constitutes the refereed proceedings of the 11th International Conference on Optimization and Applications, OPTIMA 2020, held in Moscow, Russia, in September-October 2020.*The 21 full and 2 short papers presented were carefully reviewed and selected from 60 submissions. The papers cover such topics as mathematical programming, combinatorial and discrete optimization, optimal control, optimization in economics, finance, and social sciences, global optimization, and applications. * The conference was held virtually due to the COVID-19 pandemic.
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence Applications (Intelligent Data-Driven Systems and Artificial Intelligence)
by Irfan Ali Umar Muhammad Modibbo Asaju La’aro Bolaji Harish GargThis book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility.This book: Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing Presents a comparative analysis of machine learning algorithms for power consumption prediction Discusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment Illustrates a data-driven optimization concept for modeling environmental and economic sustainability Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Optimization Applications (Intelligent Data-Driven Systems and Artificial Intelligence)
by Irfan Ali Umar Muhammad Modibbo Asaju La’aro Bolaji Harish GargThis book comprehensively discusses nature‑inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‑objective optimization under Fermatean hesitant fuzzy and uncertain environment.This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem Presents an overview of artificial intelligence (AI) and explainable AI decision‑making (XAIDM) and illustrates a data‑driven optimization concept for modeling environmental and economic sustainability Discusses machine learning‑based multi‑objective optimization technique for load balancing in integrated fog‑cloud environment Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‑estimation of functional regression operator, and intuitionistic fuzzy sets applications The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
Optimization and Control of Bilinear Systems
by Panos M. Pardalos Vitaliy A. YatsenkoCovers developments in bilinear systems theory Focuses on the control of open physical processes functioning in a non-equilibrium mode Emphasis is on three primary disciplines: modern differential geometry, control of dynamical systems, and optimization theory Includes applications to the fields of quantum and molecular computing, control of physical processes, biophysics, superconducting magnetism, and physical information science
Optimization and Data Science: 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event (AIRO Springer Series #6)
by Adriano Masone Veronica Dal Sasso Valentina MorandiThis proceedings volume collects contributions from the 5th AIRO Young Workshop and AIRO PhD School 2021 joint event on “Optimization and Data Science: Trends and Applications”, held online, from February 8 to 12, 2021. The joint event was organized by AIROYoung representatives and the Operations Research Group of the Department of Electrical Engineering and Information Technology of the University “Federico II” of Naples.The selected contributions represent the state-of-the-art knowledge related to different branches of research, such as data science, machine learning and combinatorial optimization. Therefore, this book is primarily addressed to researchers and PhD students of the operations research community. However, due to its interdisciplinary content, it will be of high interest for other closely related research communities. Moreover, this volume not only presents theoretical results but also covers real applications in computer science, engineering, economics, healthcare, and logistics, making it interesting for practitioners facing complex decision-making problems in these areas.
Optimization and Data Science in Industrial Engineering: First International Conference, ODSIE 2023, Istanbul, Turkey, November 16–17, 2023, Proceedings, Part II (Communications in Computer and Information Science #2205)
by A. Mirzazadeh Zohreh Molamohamadi Efran Babaee Tirkolaee Gerhard-Wilhelm Weber Janny LeungThis two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023. The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The papers were organized in the following topical sections: Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare. Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.
Optimization and Data Science in Industrial Engineering: First International Conference, ODSIE 2023, Istanbul, Turkey, November 16–17, 2023, Proceedings, Part I (Communications in Computer and Information Science #2204)
by A. Mirzazadeh Zohreh Molamohamadi Efran Babaee Tirkolaee Gerhard-Wilhelm Weber Janny LeungThis two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023. The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions. The papers were organized in the following topical sections: Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare. Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.
Optimization and Decision Science: ODS, Virtual Conference, November 19, 2020 (AIRO Springer Series #7)
by Raffaele Cerulli Mauro Dell’Amico Francesca Guerriero Dario Pacciarelli Antonio SforzaThis book collects selected contributions from the international conference “Optimization and Decision Science” (ODS2020), which was held online on November 19, 2020, and organized by AIRO, the Italian Operations Research Society. The book offers new and original contributions on optimization, decisions science and prescriptive analytics from both a methodological and applied perspective, using models and methods based on continuous and discrete optimization, graph theory and network optimization, analytics, multiple criteria decision making, heuristics, metaheuristics, and exact methods.In addition to more theoretical contributions, the book chapters describe models and methods for addressing a wide diversity of real-world applications, spanning health, transportation, logistics, public sector, manufacturing, and emergency management.Although the book is aimed primarily at researchers and PhD students in the Operations Research community, the interdisciplinary content makes it interesting for practitioners facing complex decision-making problems in the afore-mentioned areas, as well as for scholars and researchers from other disciplines, including artificial intelligence, computer sciences, economics, mathematics, and engineering.
Optimization and Inventory Management (Asset Analytics)
by Nita H. Shah Mandeep MittalThis book discusses inventory models for determining optimal ordering policies using various optimization techniques, genetic algorithms, and data mining concepts. It also provides sensitivity analyses for the models’ robustness. It presents a collection of mathematical models that deal with real industry scenarios. All mathematical model solutions are provided with the help of various optimization techniques to determine optimal ordering policy. The book offers a range of perspectives on the implementation of optimization techniques, inflation, trade credit financing, fuzzy systems, human error, learning in production, inspection, green supply chains, closed supply chains, reworks, game theory approaches, genetic algorithms, and data mining, as well as research on big data applications for inventory management and control. Starting from deterministic inventory models, the book moves towards advanced inventory models. The content is divided into eight major sections: inventory control and management – inventory models with trade credit financing for imperfect quality items; environmental impact on ordering policies; impact of learning on the supply chain models; EOQ models considering warehousing; optimal ordering policies with data mining and PSO techniques; supply chain models in fuzzy environments; optimal production models for multi-items and multi-retailers; and a marketing model to understand buying behaviour. Given its scope, the book offers a valuable resource for practitioners, instructors, students and researchers alike. It also offers essential insights to help retailers/managers improve business functions and make more accurate and realistic decisions.
Optimization and Its Applications in Control and Data Sciences
by Boris GoldengorinThis book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference "Optimization and Its Applications in Control and Data Science" dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak's fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.
Optimization and Learning: 4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings (Communications in Computer and Information Science #1443)
by Bernabé Dorronsoro Lionel Amodeo Mario Pavone Patricia RuizThis volume constitutes the refereed proceedings of the 4th International Conference on Optimization and Learning, OLA 2021, held in Catania, Italy, in June 2021. Due to the COVID-19 pandemic the conference was held online. The 27 full papers were carefully reviewed and selected from 62 submissions. The papers presented in the volume are organized in topical sections on synergies between optimization and learning; learning for optimization; machine learning and deep learning; transportation and logistics; optimization; applications of learning and optimization methods.
Optimization and Learning: 5th International Conference, OLA 2022, Syracuse, Sicilia, Italy, July 18–20, 2022, Proceedings (Communications in Computer and Information Science #1684)
by Bernabé Dorronsoro Mario Pavone Amir Nakib El-Ghazali TalbiThis book constitutes the refereed proceedings of the 5th International Conference on Optimization and Learning, OLA 2022, which took place in Syracuse, Sicilia, Italy, in July 2022. The 19 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: Optimization and Learning; Novel Optimization Techniques; Logistics; and Applications.
Optimization and Learning: Third International Conference, OLA 2020, Cádiz, Spain, February 17–19, 2020, Proceedings (Communications in Computer and Information Science #1173)
by Bernabé Dorronsoro Patricia Ruiz El-Ghazali Talbi Juan Carlos de la Torre Daniel UrdaThis volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods, identifying and exploiting their synergies,and analyzing their applications in different fields, such as health, industry 4.0, games, logistics, etc.
Optimization and Learning: 7th International Conference, OLA 2024, Dubrovnik, Croatia, May 13–15, 2024, Revised Selected Papers (Communications in Computer and Information Science #2311)
by Bernabé Dorronsoro Martin Zagar El-Ghazali TalbiThis book constitutes the refereed proceedings of the 7th International Conference on Optimization and Learning, OLA 2024, held in Dubrovnik, Croatia, during May 13–15, 2024. The 24 full papers presented here were carefully reviewed and selected from 64 submissions. They were organized in the following topical sections: synergies between optimization and machine learning; enhancing optimization and learning techniques; transportation and routing; and applications.
Optimization and Machine Learning: Optimization for Machine Learning and Machine Learning for Optimization
by Rachid Chelouah Patrick SiarryMachine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.
Optimization Based Data Mining: Theory and Applications
by Yong Shi Yingjie Tian Yi Peng Jianping Li Gang KouOptimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors' research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
Optimization by GRASP: Greedy Randomized Adaptive Search Procedures
by Mauricio G.C. Resende Celso C. RibeiroThis is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems.
Optimization, Control, and Applications in the Information Age
by Athanasios Migdalas Athanasia KarakitsiouRecent developments in theory, algorithms, and applications in optimization and control are discussed in this proceedings, based on selected talks from the 'Optimization Control and Applications in the Information Age' conference, organized in honor of Panos Pardalos's 60th birthday. This volume contains numerous applications to optimal decision making in energy production and fuel management, data mining, logistics, supply chain management, market network analysis, risk analysis, and community network analysis. In addition, a short biography is included describing Dr. Pardalos's path from a shepherd village on the high mountains of Thessaly to academic success. Due to the wide range of topics such as global optimization, combinatorial optimization, game theory, stochastics and programming contained in this publication, scientists, researchers, and students in optimization, operations research, analytics, mathematics and computer science will be interested in this volume.
Optimization for Computer Vision: An Introduction to Core Concepts and Methods
by Marco Alexander TreiberThis practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.